Data processing units, or DPUs, have emerged as a new computing pillar in the ever-evolving landscape of modern computing, preceded by central processing units (CPUs), and graphics processing units (GPUs).
DPUs work with CPUs and GPUs to enhance computing power and the handling of increasingly complex modern data workloads. The DPU market has steadily gained traction thanks to a rise in demand for AI, machine learning, deep learning, IoT, 5G, and complex cloud architectures. If your team is involved in projects involving advanced computing, chances are you could benefit from incorporating DPUs into your data center architecture.
In this article, you’ll get a foundational understanding of what a DPU is, how DPUs work, the benefits of DPUs, how to choose the right DPU vendor, and FAQs.
- What Is a DPU?
- What Are the Functions and Benefits of DPUs?
- What Are the Features of DPUs?
- How to Choose the Right DPU Vendor for Your Needs
- Frequently Asked Questions
What Is a DPU?
A DPU, or data processing unit, is a programmable processor designed to efficiently handle data-centric workloads such as data transfer, reduction, security, compression, analytics, and encryption, at scale in data centers.
What Are the Functions and Benefits of DPUs?
DPUs are rapidly becoming an essential component in modern computing because of their ability to improve the efficiency and performance of data centers by offloading workloads from the CPU. (For the difference between a CPU, GPU and DPU, see the FAQ section below.)
DPUs provide many benefits in modern data centers by performing the following main functions:
- Increased processing power: DPUs offload network and communication workloads from the CPU, freeing up resources for application processing.
- Increased efficiency and performance: By combining processing cores with hardware accelerator blocks to handle data-centric workloads at scale, DPUs improve performance and reduce latency.
- Ability to handle complex tasks: DPUs are designed to process data-intensive workloads in large-scale data centers supporting cloud environments or supercomputers driving AI, deep learning algorithms, and other data-intensive applications.
- Ability to accommodate growing needs of the data center: DPUs can be scaled to accommodate increasing workloads in volume and complexity as data center needs grow and become more intensive. Additionally, DPUs can be added to existing hardware infrastructure, allowing for a flexible and adaptable data center architecture.
- Improved reliability and availability: DPUs can provide improved reliability through features like redundancy and high availability, ensuring the continuity of critical data processing tasks in the event of hardware failures.
- Reduced costs: DPUs can reduce overall hardware costs related to managing a data center by offloading processing tasks from the CPU and handling complex tasks, thereby requiring fewer hardware components.
What Are the Features of DPUs?
DPUs have several features, including:
- High-speed networking connectivity
- High-speed packet processing
- Multi-core processing
- Memory controllers
- PCI Express Gen 4 Support
- Security features, like encryption, firewall, and VPN (virtual private network)
DPU providers use different technologies and materials in their products depending on an enterprise customer’s needs. There are three main types of DPUs: SOC-based, ASIC-based, and FPGA-based. Each are tailored to a specific application or customer system.
How to Choose the Right DPU Vendor for Your Needs
Key vendors in the DPU market include NVIDIA, Marvell, Fungible (acquired by Microsoft), Broadcom, Intel, Resnics, and AMD Pensando. Expect this list to grow as new DPU vendors enter the space to tackle the rapidly evolving needs of advanced data-centric workloads. The DPU space promises to be fierce battleground for tech giants and chip-makers in the coming years.
Choosing the right DPU vendor for your needs requires considering several dimensions, including:
- Compatibility: Are the DPU vendor’s hardware and software compatible with your existing infrastructure, thereby ensuring minimal or no disruption to daily operations?
- Workload requirements: Can the DPU provide sufficient coverage for your organization’s workload requirements in both complexity and volume?
- Ease of integration: Will the DPU integrate seamlessly into your infrastructure and software stack within a timeline that works for your organization?
- Security: Can the DPU vendor provide robust security features such as encryption, secure boot, secure firmware updates, and other security requirements your organization needs?
- Technical support and services: Is the vendor’s documentation, support team, training, and maintenance appropriate for your organization’s size and expertise?
- Vendor reputation: Does the DPU vendor have a proven track record of reliability and customer satisfaction?
- Cost: Does the DPU vendor’s licensing and pricing options fit within your budgetary needs and technical requirements?
Frequently Asked Questions
What are the differences between a DPU, CPU, and GPU?
- Function: A DPU (data processing unit), CPU (central processing unit), and GPU (graphics processing unit) are all computing processors, each performing a different function. The CPU is the main processor responsible for the overall operation of a computer system, serving as ‘the brain’ of a computer. The GPU is a specialized processor for graphics computing tasks, such as rendering 3D images or videos. The DPU is the newest processor, specializing in data-centric workloads, such as networking, storage, and security operations in data centers.
- Architecture: CPUs consist of a few powerful processing cores optimized for serial or sequential processing, meaning one task after another. GPUs have a large number of simpler cores optimized for parallel processing, meaning simultaneous tasks. DPUs combine processing cores, hardware accelerator blocks, and a high-performance network interface to process data-centric tasks at scale.
- Sample use cases: CPUs are used in nearly every computer device, from smartphones to computers to servers. GPUs are often used in gaming PCs. DPUs are primarily used in data centers.
How can a DPU be used to improve data center infrastructure?
A DPU can be used to improve data center infrastructure by increasing efficiency, enhancing data processing speed, and reducing workload on CPUs, leading to faster and more reliable data processing.
What hardware is needed to use a DPU?
To use a DPU, the server or networking device must have a compatible PCIe slot for the DPU card. The hardware should also have a compatible operating system and drivers, sufficient memory for the DPU to function properly, and reliable power and cooling.
What types of workloads can a DPU handle?
A DPU offloads network and communication workloads from the CPU by handling large-scale data processing needs. Such data-centric workloads range from data analytics, transfer, reduction, security, compression, analytics, compression, and encryption. DPUs are ideal for storage networking. Practical applications may include artificial intelligence and machine learning, big data analytics and processing, video transcoding and streaming, network traffic processing and security, and storage I/O acceleration.
What types of data acceleration engines are available for DPUs?
Data acceleration engines available for DPUs include encryption/decryption, compression/decompression, data reduction, AI/ML inferencing, and networking. These data acceleration engineers offload specific types of workloads from the CPU to improve efficiency, performance, and security.
What’s the future of the data processing unit?
Computing architecture will continue evolving as demand for data-intensive applications continues to increase, thereby requiring faster, more efficient, and more secure data processing. According to a report by Allied Market Research, the global data processing unit market is projected to reach $5.5 billion by 2031, growing at a CAGR of 26.9% from 2022 to 2031. As such, the DPU will likely transition from an optional component today to a necessary industry standard in the next generation of computing.